# Following codebook package's [vignette](https://cran.r-project.org/web/packages/codebook/vignettes/codebook_tutorial.html)
# Dependencies
library(tidyverse)
library(knitr)
library(kableExtra)
library(codebook)
library(labelled)
library(rio)
options(knitr.kable.NA = "/")
# Add labels ----
# read data
codebook_data <- read.csv("processed/4_data_participant_level_with_hand_scoring.csv")
# read data dictionary
dict <- read.csv("processed/5_data_dictionary.csv")
# add dictionary as labels
var_label(codebook_data) <- dict %>%
select(variable, label) %>%
dict_to_list()
# Add meta data ----
metadata(codebook_data)$name <- "Evaluative learning via deepfaked media"
metadata(codebook_data)$description <- "Across multiple experiments, we demonstrated that 'deepfakes' can establish automatic biases, self-reported evaluations, and behavioural intentions."
#metadata(codebook_data)$identifier <- "https://dx.doi.org/XXXXXXX"
metadata(codebook_data)$creator <- "Sean Hughes"
metadata(codebook_data)$citation <- "Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind."
metadata(codebook_data)$url <- "https://github.com/Sean-Hughes/DF-Impression-Formation--Video-and-Audio-"
# other meta data: see https://schema.org/Dataset
metadata(codebook_data)$datePublished <- "2020"
metadata(codebook_data)$spatialCoverage <- "Online"
# Create codebook ----
codebook(codebook_data)Dataset name: Evaluative learning via deepfaked media
Across multiple experiments, we demonstrated that ‘deepfakes’ can establish automatic biases, self-reported evaluations, and behavioural intentions.
Metadata for search engines
Spatial Coverage: Online
Citation: Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind.
URL: https://github.com/Sean-Hughes/DF-Impression-Formation–Video-and-Audio-
Date published: 2020
Creator:
|
name |
value |
|---|---|
|
1 |
Sean Hughes |
|
#Variables
Unique subject identifier
Distribution of values for subject
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| subject | Unique subject identifier | character | 0 | 1 | 1730 | 0 | 36 | 36 | 0 |
Experiement data was collected as part of
Distribution of values for experiment
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| experiment | Experiement data was collected as part of | numeric | 0 | 1 | 1 | 4 | 6 | 3.739306 | 1.482387 | ▆▇▇▅▅ |
What medium did the intervention (whetjer deepfaked or genuine) take (e.g., video and audio vs just audio)
Distribution of values for intervention_medium
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| intervention_medium | What medium did the intervention (whetjer deepfaked or genuine) take (e.g., video and audio vs just audio) | character | 0 | 1 | 2 | 0 | 5 | 5 | 0 |
What was the researcher-intended valence of the intervention? I.e., was the participant exposed to positive or negative messages?
Distribution of values for source_valence
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| source_valence | What was the researcher-intended valence of the intervention? I.e., was the participant exposed to positive or negative messages? | character | 0 | 1 | 2 | 0 | 8 | 8 | 0 |
Was the intervention genuine or deepfaked content?
Distribution of values for experiment_condition
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| experiment_condition | Was the intervention genuine or deepfaked content? | character | 0 | 1 | 2 | 0 | 7 | 9 | 0 |
Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance, complete_iat, complete_selfreport, and complete_intentions
Distribution of values for exclude_subject
0 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| exclude_subject | Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance, complete_iat, complete_selfreport, and complete_intentions | logical | 0 | 1 | FAL: 1421, TRU: 309 | 0.1786127 |
Participant age
Distribution of values for age
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | Participant age | numeric | 0 | 1 | 18 | 29 | 70 | 31.24335 | 9.94172 | ▇▆▃▁▁ |
Participant gender
Distribution of values for gender
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| gender | Participant gender | character | 0 | 1 | 5 | 0 | 4 | 22 | 0 |
The IAT captures automatic evaluations of Chris, the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores, following Greenwald et al 2003, and implemented using the IATScores R package. Briefly, D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks
Distribution of values for IAT_D2
141 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| IAT_D2 | The IAT captures automatic evaluations of Chris, the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores, following Greenwald et al 2003, and implemented using the IATScores R package. Briefly, D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks | numeric | 141 | 0.9184971 | -1.2 | 0.23 | 1.6 | 0.2084644 | 0.3642037 | ▁▃▇▃▁ |
Mean self-reported evaluation (positive-negative, good-bad, pleasant-unpleasant) of Chris, the character depicted in the intervention.
Distribution of values for mean_self_reported_evaluation
98 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| mean_self_reported_evaluation | Mean self-reported evaluation (positive-negative, good-bad, pleasant-unpleasant) of Chris, the character depicted in the intervention. | numeric | 98 | 0.9433526 | -3 | 0 | 3 | -0.0859804 | 2.029025 | ▇▃▃▅▇ |
Mean behavioural intentions to XXXXXX
Distribution of values for mean_intentions
1485 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| mean_intentions | Mean behavioural intentions to XXXXXX | numeric | 1485 | 0.1416185 | -2 | -1 | 2 | -0.9239184 | 1.052152 | ▇▅▃▂▁ |
Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see “3 Instructions for raters.docx”). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE.
Distribution of values for deepfake_detected
1120 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| deepfake_detected | Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see “3 Instructions for raters.docx”). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE. | logical | 1120 | 0.3526012 | FAL: 493, TRU: 117 | 0.1918033 |
XXXXXXX
Distribution of values for diagnosticity
70 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| diagnosticity | XXXXXXX | numeric | 70 | 0.9595376 | 0 | 2 | 3 | 1.966867 | 0.7643258 | ▁▃▁▇▃ |
XXXXXXX
Distribution of values for demand
131 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| demand | XXXXXXX | logical | 131 | 0.9242775 | FAL: 1585, TRU: 14 | 0.0087555 |
XXXXXXX
Distribution of values for reactance
131 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| reactance | XXXXXXX | logical | 131 | 0.9242775 | FAL: 1537, TRU: 62 | 0.0387742 |
XXXXXXX
Distribution of values for hypothesis_awareness
1672 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| hypothesis_awareness | XXXXXXX | logical | 1672 | 0.033526 | FAL: 58 | 0 |
XXXXXXX
Distribution of values for influence_awareness
946 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| influence_awareness | XXXXXXX | logical | 946 | 0.4531792 | TRU: 697, FAL: 87 | 0.8890306 |
Distribution of values for aot_actively_openminded_thinking_sum
1486 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| aot_actively_openminded_thinking_sum | numeric | 1486 | 0.1410405 | 22 | 38 | 48 | 37.40164 | 5.934706 | ▂▅▇▇▆ | / |
Distribution of values for bcti_belief_in_conspiracy_sum
1481 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| bcti_belief_in_conspiracy_sum | numeric | 1481 | 0.1439306 | 15 | 51 | 129 | 54.04819 | 23.60011 | ▇▇▆▂▁ | / |
Distribution of values for crt_analytic_thinking_sum
1236 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| crt_analytic_thinking_sum | numeric | 1236 | 0.2855491 | 0 | 4 | 7 | 3.953441 | 1.848155 | ▃▃▇▅▆ | / |
Distribution of values for ocq_overclaiming_sum
1480 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| ocq_overclaiming_sum | numeric | 1480 | 0.1445087 | 10 | 72 | 143 | 71.516 | 29.66252 | ▃▇▇▆▂ | / |
Distribution of values for ras_relgious_affliation_sum
1194 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| ras_relgious_affliation_sum | numeric | 1194 | 0.3098266 | 8 | 21 | 40 | 20.77799 | 8.32119 | ▇▆▇▅▂ | / |
Distribution of values for rei_rational_sum
1235 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| rei_rational_sum | numeric | 1235 | 0.2861272 | 15 | 52 | 70 | 50.79394 | 10.68324 | ▁▂▅▇▅ | / |
Distribution of values for rei_experiential_sum
1235 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | label |
|---|---|---|---|---|---|---|---|---|---|---|
| rei_experiential_sum | numeric | 1235 | 0.2861272 | 16 | 49 | 70 | 48.4101 | 8.768012 | ▁▂▇▇▂ | / |
XXXXXXX
Distribution of values for me_fake_news_awareness_sum
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_fake_news_awareness_sum | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 1 | 6 | 1.413934 | 1.191866 | ▇▃▂▁▁ |
XXXXXXX
Distribution of values for me_real_news_awareness_sum
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_real_news_awareness_sum | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 0 | 4 | 0.2008197 | 0.5178296 | ▇▁▁▁▁ |
XXXXXXX
Distribution of values for me_fake_news_accuracy_sum
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_fake_news_accuracy_sum | XXXXXXX | numeric | 1486 | 0.1410405 | 7 | 17 | 23 | 16.72131 | 2.614497 | ▁▂▇▇▂ |
XXXXXXX
Distribution of values for me_real_news_accuracy_sum
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_real_news_accuracy_sum | XXXXXXX | numeric | 1486 | 0.1410405 | 6 | 9 | 20 | 9.127049 | 2.460437 | ▇▆▂▁▁ |
XXXXXXX
Distribution of values for me_fake_news_sharing_sum
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_fake_news_sharing_sum | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 1 | 6 | 1.561475 | 1.768585 | ▇▂▁▂▁ |
XXXXXXX
Distribution of values for me_real_news_sharing_sum
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_real_news_sharing_sum | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 0 | 6 | 0.454918 | 0.9739489 | ▇▁▁▁▁ |
XXXXXXX
Distribution of values for aot_actively_openminded_thinking_pomp
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| aot_actively_openminded_thinking_pomp | XXXXXXX | numeric | 1486 | 0.1410405 | 19 | 69 | 100 | 66.90984 | 18.57739 | ▂▅▇▇▆ |
XXXXXXX
Distribution of values for bcti_belief_in_conspiracy_pomp
1481 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| bcti_belief_in_conspiracy_pomp | XXXXXXX | numeric | 1481 | 0.1439306 | 0 | 30 | 95 | 32.56627 | 19.67961 | ▇▇▆▂▁ |
XXXXXXX
Distribution of values for crt_analytic_thinking_pomp
1236 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| crt_analytic_thinking_pomp | XXXXXXX | numeric | 1236 | 0.2855491 | 0 | 57 | 100 | 56.47368 | 26.39254 | ▃▃▇▅▆ |
XXXXXXX
Distribution of values for ocq_overclaiming_pomp
1480 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ocq_overclaiming_pomp | XXXXXXX | numeric | 1480 | 0.1445087 | 6 | 40 | 79 | 39.728 | 16.45628 | ▃▇▇▆▂ |
XXXXXXX
Distribution of values for ras_relgious_affliation_pomp
1194 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ras_relgious_affliation_pomp | XXXXXXX | numeric | 1194 | 0.3098266 | 0 | 41 | 100 | 39.91978 | 26.0168 | ▇▆▇▅▂ |
XXXXXXX
Distribution of values for rei_rational_pomp
1235 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| rei_rational_pomp | XXXXXXX | numeric | 1235 | 0.2861272 | -4 | 27 | 42 | 25.67879 | 8.894561 | ▁▂▅▇▅ |
XXXXXXX
Distribution of values for rei_experiential_pomp
1235 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| rei_experiential_pomp | XXXXXXX | numeric | 1235 | 0.2861272 | -3 | 24 | 42 | 23.68687 | 7.323976 | ▁▂▇▇▂ |
XXXXXXX
Distribution of values for me_fake_news_awareness_pomp
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_fake_news_awareness_pomp | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 17 | 100 | 23.62295 | 19.8172 | ▇▃▂▁▁ |
XXXXXXX
Distribution of values for me_real_news_awareness_pomp
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_real_news_awareness_pomp | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 0 | 67 | 3.389344 | 8.694166 | ▇▁▁▁▁ |
XXXXXXX
Distribution of values for me_fake_news_accuracy_pomp
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_fake_news_accuracy_pomp | XXXXXXX | numeric | 1486 | 0.1410405 | 6 | 61 | 94 | 59.59426 | 14.52695 | ▁▂▇▇▂ |
XXXXXXX
Distribution of values for me_real_news_accuracy_pomp
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_real_news_accuracy_pomp | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 17 | 78 | 17.43443 | 13.57618 | ▇▆▂▁▁ |
XXXXXXX
Distribution of values for me_fake_news_sharing_pomp
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_fake_news_sharing_pomp | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 17 | 100 | 26.03279 | 29.46922 | ▇▂▁▂▁ |
XXXXXXX
Distribution of values for me_real_news_sharing_pomp
1486 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| me_real_news_sharing_pomp | XXXXXXX | numeric | 1486 | 0.1410405 | 0 | 0 | 100 | 7.602459 | 16.21796 | ▇▁▁▁▁ |
IAT_D2 recoded for source_valence. If source_valence == “negative”, IAT_D2*-1, otherwise IAT_D2.
Distribution of values for IAT_D2_recoded_for_source_valence
141 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| IAT_D2_recoded_for_source_valence | IAT_D2 recoded for source_valence. If source_valence == “negative”, IAT_D2*-1, otherwise IAT_D2. | numeric | 141 | 0.9184971 | -0.94 | 0.2 | 1.6 | 0.1854097 | 0.3764714 | ▁▅▇▃▁ |
mean_self_reported_evaluation recoded for source_valence. If source_valence == “negative”, mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation.
Distribution of values for mean_self_reported_evaluation_recoded_for_source_valence
98 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| mean_self_reported_evaluation_recoded_for_source_valence | mean_self_reported_evaluation recoded for source_valence. If source_valence == “negative”, mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation. | numeric | 98 | 0.9433526 | -3 | 2 | 3 | 1.568836 | 1.289025 | ▁▁▂▃▇ |
mean_intentions recoded for source_valence. If source_valence == “negative”, mean_intentions*-1, otherwise mean_intentions.
Distribution of values for mean_intentions_recoded_for_source_valence
1485 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| mean_intentions_recoded_for_source_valence | mean_intentions recoded for source_valence. If source_valence == “negative”, mean_intentions*-1, otherwise mean_intentions. | numeric | 1485 | 0.1416185 | -2 | 0.67 | 2 | 0.5263673 | 1.298441 | ▂▃▅▂▇ |
XXXXXXX
Distribution of values for deepfake_concept_check
1294 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| deepfake_concept_check | XXXXXXX | logical | 1294 | 0.2520231 | TRU: 234, FAL: 202 | 0.5366972 |
XXXXXXX
Distribution of values for deepfake_detected_rater_1
1120 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| deepfake_detected_rater_1 | XXXXXXX | logical | 1120 | 0.3526012 | FAL: 462, TRU: 148 | 0.242623 |
XXXXXXX
Distribution of values for deepfake_detected_rater_2
1120 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| deepfake_detected_rater_2 | XXXXXXX | logical | 1120 | 0.3526012 | FAL: 476, TRU: 134 | 0.2196721 |
XXXXXXX
Distribution of values for deepfake_concept_check_rater_1
1294 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| deepfake_concept_check_rater_1 | XXXXXXX | logical | 1294 | 0.2520231 | TRU: 247, FAL: 189 | 0.5665138 |
XXXXXXX
Distribution of values for deepfake_concept_check_rater_2
1294 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| deepfake_concept_check_rater_2 | XXXXXXX | logical | 1294 | 0.2520231 | TRU: 247, FAL: 189 | 0.5665138 |
Open ended gender
Distribution of values for gender_self_describe
1725 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| gender_self_describe | Open ended gender | character | 1725 | 0.0028902 | 1 | 0 | 4 | 4 | 0 |
Ethnicity
Distribution of values for ethnicity
1189 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| ethnicity | Ethnicity | character | 1189 | 0.3127168 | 7 | 0 | 5 | 25 | 0 |
Open ended ethnicity
Distribution of values for ethnicity_self_describe
1701 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| ethnicity_self_describe | Open ended ethnicity | character | 1701 | 0.016763 | 19 | 0 | 4 | 40 | 0 |
Location
Distribution of values for location
1190 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| location | Location | character | 1190 | 0.3121387 | 18 | 0 | 5 | 24 | 0 |
Education
Distribution of values for education
1189 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| education | Education | character | 1189 | 0.3127168 | 8 | 0 | 7 | 21 | 0 |
Education recoded into XXXXXXXX groups
Distribution of values for education_recoded
1189 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| education_recoded | Education recoded into XXXXXXXX groups | numeric | 1189 | 0.3127168 | 1 | 5 | 7 | 4.072089 | 1.526427 | ▅▅▁▇▃ |
Employment status
Distribution of values for employment
1189 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| employment | Employment status | character | 1189 | 0.3127168 | 10 | 0 | 7 | 24 | 0 |
Income level
Distribution of values for income
1190 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| income | Income level | character | 1190 | 0.3121387 | 10 | 0 | 12 | 20 | 0 |
Income level recoded into XXXXX groups
Distribution of values for income_recoded
1238 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| income_recoded | Income level recoded into XXXXX groups | numeric | 1238 | 0.2843931 | 1 | 3 | 8 | 2.943089 | 1.503835 | ▇▆▇▁▁ |
XXXXXXX
Distribution of values for political_ideology_identity
1190 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| political_ideology_identity | XXXXXXX | numeric | 1190 | 0.3121387 | -3 | 1 | 3 | 0.8574074 | 1.544648 | ▂▂▃▇▇ |
XXXXXXX
Distribution of values for political_ideology_economic_issues
1190 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| political_ideology_economic_issues | XXXXXXX | numeric | 1190 | 0.3121387 | -2 | 0 | 2 | -0.4148148 | 1.032929 | ▃▇▇▃▁ |
XXXXXXX
Distribution of values for religious_affiliation_general
1190 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| religious_affiliation_general | XXXXXXX | character | 1190 | 0.3121387 | 10 | 0 | 4 | 16 | 0 |
XXXXXXX
Distribution of values for religious_affiliation_general_recoded
1190 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| religious_affiliation_general_recoded | XXXXXXX | character | 1190 | 0.3121387 | 3 | 0 | 7 | 9 | 0 |
Mark participants for exclusion if their total error rate is >30%, their error rate in any one block is >40%, or if >10% RTs are <300ms.
Distribution of values for passed_iat_performance
122 missing values.
| name | label | data_type | n_missing | complete_rate | count | mean |
|---|---|---|---|---|---|---|
| passed_iat_performance | Mark participants for exclusion if their total error rate is >30%, their error rate in any one block is >40%, or if >10% RTs are <300ms. | logical | 122 | 0.9294798 | TRU: 1440, FAL: 168 | 0.8955224 |
Complete IAT data (used for exclusions)
Distribution of values for complete_iat
122 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| complete_iat | Complete IAT data (used for exclusions) | character | 122 | 0.9294798 | 3 | 0 | 6 | 8 | 0 |
Complete self reported evaluations data (used for exclusions)
Distribution of values for complete_selfreport
98 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| complete_selfreport | Complete self reported evaluations data (used for exclusions) | character | 98 | 0.9433526 | 2 | 0 | 6 | 8 | 0 |
Complete behavioural intentions data (used for exclusions)
Distribution of values for complete_intentions
1485 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| complete_intentions | Complete behavioural intentions data (used for exclusions) | character | 1485 | 0.1416185 | 2 | 0 | 6 | 8 | 0 |
Did the particiapant complete the self-reported evaluations or the IAT first?
Distribution of values for task_order
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| task_order | Did the particiapant complete the self-reported evaluations or the IAT first? | character | 0 | 1 | 2 | 0 | 9 | 25 | 0 |
Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g., if source_valence was negative, did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)?
Distribution of values for iat_block_order
0 missing values.
| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| iat_block_order | Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g., if source_valence was negative, did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)? | character | 0 | 1 | 2 | 0 | 31 | 33 | 0 |
JSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "Evaluative learning via deepfaked media",
"description": "Across multiple experiments, we demonstrated that 'deepfakes' can establish automatic biases, self-reported evaluations, and behavioural intentions.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"creator": "Sean Hughes",
"citation": "Hughes, S., Fried, O., Ferguson, M. J., Yao, D., Hughes, C., Hughes, R., & Hussey, I. (2020). Using Deepfakes to Hack the Human Mind.",
"url": "https://github.com/Sean-Hughes/DF-Impression-Formation--Video-and-Audio-",
"datePublished": "2020",
"spatialCoverage": "Online",
"keywords": ["subject", "experiment", "intervention_medium", "source_valence", "experiment_condition", "exclude_subject", "age", "gender", "IAT_D2", "mean_self_reported_evaluation", "mean_intentions", "deepfake_detected", "diagnosticity", "demand", "reactance", "hypothesis_awareness", "influence_awareness", "aot_actively_openminded_thinking_sum", "bcti_belief_in_conspiracy_sum", "crt_analytic_thinking_sum", "ocq_overclaiming_sum", "ras_relgious_affliation_sum", "rei_rational_sum", "rei_experiential_sum", "me_fake_news_awareness_sum", "me_real_news_awareness_sum", "me_fake_news_accuracy_sum", "me_real_news_accuracy_sum", "me_fake_news_sharing_sum", "me_real_news_sharing_sum", "aot_actively_openminded_thinking_pomp", "bcti_belief_in_conspiracy_pomp", "crt_analytic_thinking_pomp", "ocq_overclaiming_pomp", "ras_relgious_affliation_pomp", "rei_rational_pomp", "rei_experiential_pomp", "me_fake_news_awareness_pomp", "me_real_news_awareness_pomp", "me_fake_news_accuracy_pomp", "me_real_news_accuracy_pomp", "me_fake_news_sharing_pomp", "me_real_news_sharing_pomp", "IAT_D2_recoded_for_source_valence", "mean_self_reported_evaluation_recoded_for_source_valence", "mean_intentions_recoded_for_source_valence", "deepfake_concept_check", "deepfake_detected_rater_1", "deepfake_detected_rater_2", "deepfake_concept_check_rater_1", "deepfake_concept_check_rater_2", "gender_self_describe", "ethnicity", "ethnicity_self_describe", "location", "education", "education_recoded", "employment", "income", "income_recoded", "political_ideology_identity", "political_ideology_social_issues", "political_ideology_economic_issues", "religious_affiliation_general", "religious_affiliation_general_recoded", "passed_iat_performance", "complete_iat", "complete_selfreport", "complete_intentions", "task_order", "iat_block_order"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "subject",
"description": "Unique subject identifier",
"@type": "propertyValue"
},
{
"name": "experiment",
"description": "Experiement data was collected as part of",
"@type": "propertyValue"
},
{
"name": "intervention_medium",
"description": "What medium did the intervention (whetjer deepfaked or genuine) take (e.g., video and audio vs just audio)",
"@type": "propertyValue"
},
{
"name": "source_valence",
"description": "What was the researcher-intended valence of the intervention? I.e., was the participant exposed to positive or negative messages?",
"@type": "propertyValue"
},
{
"name": "experiment_condition",
"description": "Was the intervention genuine or deepfaked content?",
"@type": "propertyValue"
},
{
"name": "exclude_subject",
"description": "Should the participant be excluded (TRUE) or retained (FALSE) based on passed_iat_performance, complete_iat, complete_selfreport, and complete_intentions",
"@type": "propertyValue"
},
{
"name": "age",
"description": "Participant age",
"@type": "propertyValue"
},
{
"name": "gender",
"description": "Participant gender",
"@type": "propertyValue"
},
{
"name": "IAT_D2",
"description": "The IAT captures automatic evaluations of Chris, the character depicted in the intervention. Accuracies and reaction times on IAT were converted to D2 scores, following Greenwald et al 2003, and implemented using the IATScores R package. Briefly, D2 is a standardized difference score of trimmed reaction times in one block type versus the other: (mean_block_2 - mean_block_1) / standard_deviation_all_blocks",
"@type": "propertyValue"
},
{
"name": "mean_self_reported_evaluation",
"description": "Mean self-reported evaluation (positive-negative, good-bad, pleasant-unpleasant) of Chris, the character depicted in the intervention.",
"@type": "propertyValue"
},
{
"name": "mean_intentions",
"description": "Mean behavioural intentions to XXXXXX",
"@type": "propertyValue"
},
{
"name": "deepfake_detected",
"description": "Open ended responses to the deepfaking detection question were hand scored by two independent reviewers (see \"3 Instructions for raters.docx\"). If both reviewers scored an open ended response as having detected the intervention as a deepfake (whether correctly or not), this variable was set to TRUE. Otherwise FALSE.",
"@type": "propertyValue"
},
{
"name": "diagnosticity",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "demand",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "reactance",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "hypothesis_awareness",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "influence_awareness",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "aot_actively_openminded_thinking_sum",
"@type": "propertyValue"
},
{
"name": "bcti_belief_in_conspiracy_sum",
"@type": "propertyValue"
},
{
"name": "crt_analytic_thinking_sum",
"@type": "propertyValue"
},
{
"name": "ocq_overclaiming_sum",
"@type": "propertyValue"
},
{
"name": "ras_relgious_affliation_sum",
"@type": "propertyValue"
},
{
"name": "rei_rational_sum",
"@type": "propertyValue"
},
{
"name": "rei_experiential_sum",
"@type": "propertyValue"
},
{
"name": "me_fake_news_awareness_sum",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_real_news_awareness_sum",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_fake_news_accuracy_sum",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_real_news_accuracy_sum",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_fake_news_sharing_sum",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_real_news_sharing_sum",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "aot_actively_openminded_thinking_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "bcti_belief_in_conspiracy_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "crt_analytic_thinking_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "ocq_overclaiming_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "ras_relgious_affliation_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "rei_rational_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "rei_experiential_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_fake_news_awareness_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_real_news_awareness_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_fake_news_accuracy_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_real_news_accuracy_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_fake_news_sharing_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "me_real_news_sharing_pomp",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "IAT_D2_recoded_for_source_valence",
"description": "IAT_D2 recoded for source_valence. If source_valence == \"negative\", IAT_D2*-1, otherwise IAT_D2.",
"@type": "propertyValue"
},
{
"name": "mean_self_reported_evaluation_recoded_for_source_valence",
"description": "mean_self_reported_evaluation recoded for source_valence. If source_valence == \"negative\", mean_self_reported_evaluation*-1, otherwise mean_self_reported_evaluation.",
"@type": "propertyValue"
},
{
"name": "mean_intentions_recoded_for_source_valence",
"description": "mean_intentions recoded for source_valence. If source_valence == \"negative\", mean_intentions*-1, otherwise mean_intentions.",
"@type": "propertyValue"
},
{
"name": "deepfake_concept_check",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "deepfake_detected_rater_1",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "deepfake_detected_rater_2",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "deepfake_concept_check_rater_1",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "deepfake_concept_check_rater_2",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "gender_self_describe",
"description": "Open ended gender",
"@type": "propertyValue"
},
{
"name": "ethnicity",
"description": "Ethnicity",
"@type": "propertyValue"
},
{
"name": "ethnicity_self_describe",
"description": "Open ended ethnicity",
"@type": "propertyValue"
},
{
"name": "location",
"description": "Location",
"@type": "propertyValue"
},
{
"name": "education",
"description": "Education",
"@type": "propertyValue"
},
{
"name": "education_recoded",
"description": "Education recoded into XXXXXXXX groups",
"@type": "propertyValue"
},
{
"name": "employment",
"description": "Employment status",
"@type": "propertyValue"
},
{
"name": "income",
"description": "Income level",
"@type": "propertyValue"
},
{
"name": "income_recoded",
"description": "Income level recoded into XXXXX groups",
"@type": "propertyValue"
},
{
"name": "political_ideology_identity",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "political_ideology_social_issues",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "political_ideology_economic_issues",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "religious_affiliation_general",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "religious_affiliation_general_recoded",
"description": "XXXXXXX",
"@type": "propertyValue"
},
{
"name": "passed_iat_performance",
"description": "Mark participants for exclusion if their total error rate is >30%, their error rate in any one block is >40%, or if >10% RTs are <300ms.",
"@type": "propertyValue"
},
{
"name": "complete_iat",
"description": "Complete IAT data (used for exclusions)",
"@type": "propertyValue"
},
{
"name": "complete_selfreport",
"description": "Complete self reported evaluations data (used for exclusions)",
"@type": "propertyValue"
},
{
"name": "complete_intentions",
"description": "Complete behavioural intentions data (used for exclusions)",
"@type": "propertyValue"
},
{
"name": "task_order",
"description": "Did the particiapant complete the self-reported evaluations or the IAT first?",
"@type": "propertyValue"
},
{
"name": "iat_block_order",
"description": "Was the participant exposed to the intervention-consistent or intervention-inconsistent block in the IAT first? E.g., if source_valence was negative, did the IAT map Chris and negative to the same key (con) or Chris and positive (incon)?",
"@type": "propertyValue"
}
]
}`Original csv file is used for analyses (as it is simplest), but other file types that integrate the labels are likely to be more useful for reuse.
I include an R .rds file (which includes data labels and data types), SPSS .sav and Stata .dta files.
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rio_0.5.16 labelled_2.7.0 codebook_0.9.2 kableExtra_1.3.1
## [5] knitr_1.30 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
## [9] purrr_0.3.4 readr_1.3.1 tidyr_1.1.2 tibble_3.0.3
## [13] ggplot2_3.3.2 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.1 jsonlite_1.7.1 viridisLite_0.3.0 modelr_0.1.8
## [5] assertthat_0.2.1 highr_0.8 cellranger_1.1.0 yaml_2.2.1
## [9] globals_0.13.1 pillar_1.4.6 backports_1.1.9 glue_1.4.2
## [13] digest_0.6.25 rvest_0.3.5 colorspace_1.4-1 htmltools_0.5.0
## [17] pkgconfig_2.0.3 broom_0.7.2 listenv_0.8.0 haven_2.3.1
## [21] scales_1.1.1 webshot_0.5.2 openxlsx_4.1.5 generics_0.0.2
## [25] farver_2.0.3 ellipsis_0.3.1 DT_0.13 withr_2.2.0
## [29] repr_1.1.0 skimr_2.1.2 cli_2.0.2 rmdpartials_0.5.8
## [33] magrittr_1.5 crayon_1.3.4 readxl_1.3.1 evaluate_0.14
## [37] fs_1.4.1 future_1.19.1 fansi_0.4.1 xml2_1.3.2
## [41] foreign_0.8-80 tools_4.0.2 data.table_1.13.2 hms_0.5.3
## [45] lifecycle_0.2.0 munsell_0.5.0 reprex_0.3.0 zip_2.1.1
## [49] compiler_4.0.2 rlang_0.4.8 grid_4.0.2 rstudioapi_0.11
## [53] htmlwidgets_1.5.1 crosstalk_1.1.0.1 base64enc_0.1-3 labeling_0.3
## [57] rmarkdown_2.5 gtable_0.3.0 codetools_0.2-16 DBI_1.1.0
## [61] curl_4.3 R6_2.4.1 lubridate_1.7.9 stringi_1.4.6
## [65] parallel_4.0.2 Rcpp_1.0.5 vctrs_0.3.4 dbplyr_1.4.3
## [69] tidyselect_1.1.0 xfun_0.15